Digitalization and Datafication:Everyday Management of Menstrual Period

The range of self-tracking digital technologies is very wide: mobile apps available to download; wearable technologies – Google Glass, Fitbit – that can be worn as bracelets or clipped onto clothes; sensors that can be embedded not only in the device for recording biometrics information (i.e. body temperature, hearth rate, blood glucose, etc.), but also in the smart city in order to monitor air pollution, traffic, energy consumption and so on. Self-tracking technologies allow users to track and transform into data – statistical analysis and graphical representations – daily information, practices and activities: calories intake, workout exercises, weight, mood, cigarettes or drink intake, financial expenses, social interaction, social media activities, sleeping hours, chronic diseases, health of urban environment, sexual and reproductive health, etc. The paper is constructed around two main questions: (1) how do self-tracking technologies intra-act with the embodiment of Self? (2) How does expert medical knowledge, inscribed in self-tracking technologies, perform body and personal bodily knowledge. Therefore, after an overview of theoretical framework, the second section provides an exploratory empirical analysis of the period tracker apps’ uses. Thus, the empirical part focuses on the women entanglement in the management of cycle through self-tracking apps that are aimed to map and transform into data daily symptoms and mood in order to visualize correlations and predict fertile windows, PMS and future menstrual periods

Data Analytics in Chronic Disease Self-Management: Statistical and Machine Learning Methodologies for Knowledge Discovery based on Quantified Self Data

Η διαχείριση των χρόνιων παθήσεων συνιστά μια από τις σημαντικότερες προκλήσεις των σύγχρονων συστημάτων υγείας. Η επιτακτική ανάγκη της συνεχούς διαχείρισης των νοσημάτων αυτών, που συνιστούν αιτία θανάτου για περισσότερο από το 70% του πληθυσμού παγκοσμίως [1], ήταν ένας από τους λόγους που οδήγησαν τον τομέα της ηλεκτρονικής υγείας να γνωρίσει ραγδαία ανάπτυξη. Παράλληλα, η ιδέα της αυτοδιαχείρισης προσωπικών δεδομένων υγείας και τρόπου ζωής, υπό το πρίσμα των νέων τεχνολογιών, κερδίζει έδαφος πολύ γρήγορα. Στις μέρες μας, οι αισθητήρες συνιστούν αναπόσπαστο κομμάτι της καθημερινότητας και συλλέγουν τεράστιες ποσότητες δεδομένων, ελέγχοντας κάθε πτυχή αυτής. Η πρόκληση, λοιπόν, είναι πώς θα καταφέρουμε να διαχειριστούμε όλα αυτά τα δεδομένα που προκύπτουν από το συνδυασμό των υπηρεσιών ηλεκτρονικής υγείας με τις τεχνολογίες φορετών αισθητήρων και κυρίως πώς θα τα ερμηνεύσουμε, ώστε να διευρύνουμε τους ορίζοντες της επιστημονικής έρευνας [2]. Στο σημείο αυτό, ο τομέας της ανάλυσης δεδομένων καλείται να αναλάβει καθοριστικό ρόλο. Οι ασθενείς που χρησιμοποιούν τέτοιες τεχνολογίες, αποκτούν τη δυνατότητα να καταγράψουν και να επεξεργαστούν τα βιοσήματά τους, τις αθλητικές τους δραστηριότητες, τις καθημερινές συνήθειές τους ή ακόμα και τα συναισθήματά τους [3]. Τα δεδομένα που προκύπτουν συνιστούν τον πολύτιμο λίθο της στατιστικής και των τεχνικών μηχανικής μάθησης, η εφαρμογή των οποίων θα οδηγήσει σε εξόρυξη γνώσεων σχετικά με τους παράγοντες αυξημένης επικινδυνότητας για την υγεία ενός ασθενούς και θα παράσχει τη δυνατότητα εξατομικευμένης ιατρικής παρακολούθησης και άμεσης ενημέρωσης για αποφυγή επειγόντων περιστατικών. Η παρούσα διπλωματική εργασία προτείνει μια μεθοδολογία ανάλυσης δεδομένων που θα εξετάσει τη συνέπεια των ασθενών στο πρόγραμμα λήψης των μετρήσεών τους και θα μελετήσει την αλληλεπίδραση μεταξύ των διαφορετικών ημερήσιων μετρήσεων, με σκοπό τον προσδιορισμό του τρόπου με τον οποίο αυτοί οι παράγοντες μπορούν να επηρεάσουν την παρακολούθηση της υγείας των ασθενών. Παράλληλα, θα πραγματοποιηθούν μελέτες που γενικεύονται σε δημογραφικό επίπεδο, συμπεριλαμβα-νομένου του φύλου, της ηλικίας και της γεωγραφικής κατανομής, έτσι ώστε να εντοπιστούν οι στατιστικά σημαντικές διαφορές στις ιατρικές τιμές ανα πληθυσμιακή ομάδα και να εξαχθούν πιο στοχευμένα, κατάλληλα συμπεράσματα. Στοχεύοντας στη βελτίωση και εξατομίκευση της ιατρικής παρακολούθησης χρόνιων καταστάσεων υγείας, η προτεινόμενη λύση δύναται να αντιμετωπίσει τις προκλήσεις των ηλεκτρονικών υπηρεσιών υγείας, παρέχοντας στους ασθενείς τη δυνατότητα έγκαιρου εντοπισμού επικίνδυνων καταστάσεων, ενίσχυση της ευημερίας τους, κινητοποίηση για συμμόρφωση στο πρόγραμμα λήψης των μετρήσεών τους αλλά και την εξειδικευμένη θεραπευτική τους αγωγή, δέσμευση για άσκηση και, τέλος, μοντελοποίηση της συμπεριφοράς τους με σκοπό τη βελτίωση της φροντίδας του εαυτού τους και την απόκτηση μιας καλύτερης ποιότητας ζωής.Chronic diseases management is one of the greatest challenges of modern healthcare systems. Given the fact that non-communicable diseases are responsible for more than 70% of deaths worldwide [1], the constant monitoring of a patient’s health condition has become vital need and, hence, the era of mobile health starts to rise. At the same time, the idea of self-managing personal aspects of life, and not only, through the prism of new technologies, the so-called Quantified Self, gains ground rapidly. Nowadays, sensors constitute an integral part of the daily life and monitor almost every aspect of it, gathering enormous quantities of data. The challenge is how to control the data that derive from the combination of electronic health services with wearable sensor technologies and broaden the horizons of scientific research [2]. At this point, data analytics assumes its decisive role. Patients using such technologies gain the capability to record and process their vital signs, fitness activities, everyday habits, or even feelings [3]. The resulting data constitute the gemstone for statistical and machine learning techniques to be performed so that knowledge discovery can take place and, as a consequence, identify the risk factors in patients’ health and provide personalized medical follow-up and immediate feedback to avoid emergent situations. This graduate thesis proposes a data analytics solution that will examine patients’ consistency in their measurement schedule and study the interaction among the different daily measurements, with the scope of determining how these factors can influence the monitoring of their health. Studies generalized on a demographic level, including sex, age and geolocation, will also take place so that statistical significant differences can be identified in the medical values and, thus, appropriate recommendations can be derived per population group. Aiming at improving and personalizing the medical monitoring of chronic health conditions, the proposed solution can circumvent the challenges of electronic health systems and provide benefits for the involved patients, such as enhancement of their welfare, early detection of dangerous situations, assumption of further targeted monitoring, motivation to engage in self-caring activities and follow treatment and, last, modeling of their behavior to improve self-care and enjoy a better quality of life

A literature review of wellness, wellbeing and quality of life issues as they impact upon the Australian mining sector

Australia’s mining boom Global demand for minerals and energy products has fuelled Australia’s recent resources boom and has led to the rapid expansion of mining projects not only in remote locations but increasingly in settled traditionally agricultural rural areas. A fundamental shift has also occurred in the provisioning of skilled and semi-skilled workers. The huge acceleration in industry demand for labour has been accompanied by the entrenchment of workforce arrangements largely dependent on fly-in, fly-out (FIFO) and drive–in, drive–out (DIDO) non-resident workers (NRWs). While NRWs are working away from their homes, they are usually accommodated in work camps or ‘villages’ for the duration of their work cycle which are normally comprised of many consecutive days of 12-hour day- and night-shifts. The health effects of this form of employment and the accompanying lifestyle is increasingly becoming contentious. Impacts on personal wellness, wellbeing and quality of life essentially remain under-researched and thus misunderstood. Sodexo in Australia Sodexo began operations in Australia in 1982, and has since become a leader in providing Quality of Life (QOL) services to businesses across the country. The 6,000 Australian employees are part of a global Sodexo team of 413,000 people. Sodexo in Australia designs, delivers and manages on-site their QOL services at 320 diverse site locations, including remote sites. Sodexo operates in a range of sectors, including the mining industry. Service plans are tailored to suit the individual needs of organisations. Sodexo Remote Sites has previously conducted unpublished research among mining workers in Australia. The results highlighted needs and expectations of Australian mining workers. Main insights about workers’ requirements were directed towards: • contacts with closest; • warm rest time around proper and varied meals; • additional services to help them better enjoy their life onsite and/or make the most of it; • organise their transportation; • promote community living; and • finding balance between professional and personal life. The brief for this current research is aimed at building upon this knowledge. Research brief Expectations for quality of life and wellness and wellbeing services are increasing dramatically. It's getting costlier and more difficult to retain valuable employees. This is particularly the case in the Australian mining sector. Given the level of interest in ensuring healthy workplaces in Australia, Sodexo has commissioned QUT to conduct a literature review. The objectives as specified by Sodexo are: Objective 1: To define the concepts of wellness and wellbeing and quality of life in Australia Objective 2: To examine how wellness and wellbeing are developed within organisations in Australia and how they impact on employee and organizational performance. More specifically, to review the literature that could be sourced about: • challenges of the mining environment; • the mining lifestyle – implications for health, wellness and daily life; • personal health and wellness of Australian mining workers; • factors affecting health in mines and perceived support for health and wellness; and • the impact of employer investment in health on perceptions and behaviour of employees. Objective 3: To determine what impact employee wellness and well-being has on the performance of mining workers. More specifically, to review the literature that could be sourced about: • impact of obesity, alcohol, tobacco use on companies; and • links between employee engagement and satisfaction and company productivity. Accordingly this review has attempted to ascertain what factors an organisation should focus on in order to reduce absenteeism and turnover and increase commitment, satisfaction, safety and productivity, with specific reference to the mining industry in Australia. The structure of the report aligns with the stated objectives in that each of the first three parts address an objective. Part IV summarises prominent issues that have arisen and offers some concluding observations and comments

Internalizing Data Collection: Personal Analytics as an Investigation of the Self

Personal analytics, aka self-tracking, is the practice of using a digital device to track aspects of behavior, such as exercise habits, heart rate, sleep patterns, location, diet, and a host of other data points. This dissertation is an exploration of “self” in self-tracking, informed by theories of subjectivity, autonomy, power and knowledge. As a technological intervention, self-tracking devices change how we experience our own body and behavior. They also serve as methods to digitize human behavior. This data is combined with other data and processed using computational methods. Self-tracking devices are both personal and bureaucratic. They are devices used for self-care and institutional processes. As mediating objects, they occupy a multifaceted position that they share with other forms of mediated experience. Like social media, which is both a form of personal expression and a way to track users’ behavior, self-tracking participates in changing attitudes about surveillance. People are willing to subject themselves to surveillance and are largely unaware or unconcerned with the ways in which self-surveillance is the same thing as institutional surveillance. This study positions self-tracking as a practice of institutional population management, not simply personalized exercise tools. A Fitbit might seem to simply measure a “step,” an identifiable metric that exists regardless of whether it is counted. Yet, how can this metric be considered neutral and objective when its institutional purpose guides its development? Thinking of measurement as neutral ignores the process by which anything comes to be measured. All kinds of decisions—about what to count, how to count it, and what to do with the data—are made prior to the end user’s experience. Measurement is a cultural activity and thus the outcome of this data collection is never neutral with respect to power. By looking at fitness-tracker privacy policies, workplace wellness programs, data sharing practices, and advertising materials, I trace the discursive practices surrounding self-tracking. As we surveil our bodies and behavior, we enact a focused attention upon the self. Understanding the consequence of this focus is crucial to understanding how data operates in today’s economy. My overall critique of data in this dissertation concerns how the focus on self obscures the institutional uses and abuses of data. The epistemic affordances of data flow in multiple directions. Self-tracking devices offer the promise to reveal hidden data about the self. They accomplish something different—they create the means to recraft the self into something else entirely. They make the self into an entity that is knowable and therefore able to be the subject of market transactions and manipulated by institutions

User Satisfaction with Wearables

This study investigates user satisfaction with wearable technologies. It proposes that the integration of expectation confirmation theory with affordance theory sheds light on the sources of user’s (dis)confirmation when evaluating technology performance experiences and explains the origins of satisfaction ratings. A qualitative and quantitative analysis of online user reviews of a popular fitness wristband supports the research model. Since the band lacks buttons and numeric displays, users need to interact with the companion software to obtain the information they need. Findings indicate that satisfaction depends on the interaction’s quality, the value of digitalizing physical activity, and the extent to which the informational feedback meets users’ needs. Moreover, the results suggest that digitalizing physical activity has different effects for different users. While some appreciate data availability in general regardless of their accuracy, those who look for precision do not find such quantification useful. Thus, their evaluative judgments depend on the wearable system’s actual performance and the influence that the feedback has on their pursuit of their fitness goals. These results provide theoretical and practical contributions to advance our understanding of wearable technologies

The development of a risk index for depression (RID)

&nbsp;This thesis developed a novel methodology for a flexible and modular Risk Index for Depression (RID) that blended data mining and machine learning techniques with traditional statistical techniques. This RID shows great potential for future clinical use.<br /

Data Epistemologies / Surveillance and Uncertainty

Data Epistemologies studies the changing ways in which ‘knowledge’ is defined, promised, problematised, legitimated vis-á-vis the advent of digital, ‘big’ data surveillance technologies in early twenty-first century America. As part of the period’s fascination with ‘new’ media and ‘big’ data, such technologies intersect ambitious claims to better knowledge with a problematisation of uncertainty. This entanglement, I argue, results in contextual reconfigurations of what ‘counts’ as knowledge and who (or what) is granted authority to produce it – whether it involves proving that indiscriminate domestic surveillance prevents terrorist attacks, to arguing that machinic sensors can know us better than we can ever know ourselves. The present work focuses on two empirical cases. The first is the ‘Snowden Affair’ (2013-Present): the public controversy unleashed through the leakage of vast quantities of secret material on the electronic surveillance practices of the U.S. government. The second is the ‘Quantified Self’ (2007-Present), a name which describes both an international community of experimenters and the wider industry built up around the use of data-driven surveillance technology for self-tracking every possible aspect of the individual ‘self’. By triangulating media coverage, connoisseur communities, advertising discourse and leaked material, I examine how surveillance technologies were presented for public debate and speculation. This dissertation is thus a critical diagnosis of the contemporary faith in ‘raw’ data, sensing machines and algorithmic decision-making, and of their public promotion as the next great leap towards objective knowledge. Surveillance is not only a means of totalitarian control or a technology for objective knowledge, but a collective fantasy that seeks to mobilise public support for new epistemic systems. Surveillance, as part of a broader enthusiasm for ‘data-driven’ societies, extends the old modern project whereby the human subject – its habits, its affects, its actions – become the ingredient, the raw material, the object, the target, for the production of truths and judgments about them by things other than themselves